Panagiotis Sapountzis is an Assistant Professor of Physiology and Neurophysiology at the School of Medicine of the University of Crete. He is also an Associated Researcher at the Institute of Applied and Computational Mathematics (IACM) of FORTH.
Contact Info
| First Name | Panagiotis |
| Last Name | Sapountzis |
| pasapoyn@iacm.forth.gr | |
| CV | Curriculum-vitae |
| Webpage | https://physiology.med.uoc.gr/?n=PanosSapountzis.Contact |
| Address | School of Medicine, University of Crete |
| Phone | +30 2810 394502 |
Publications
- P. Sapountzis, A. Antoniadou, G.G. Gregoriou (2025) Diverse neuronal activity patterns contribute to the control of distraction in the prefrontal and parietal cortex, PLOS Biology, https://doi.org/10.1371/journal.pbio.3003008
- Theocharous A., Gregoriou G.G., Sapountzis P., Kontoyiannis I. (2024) Temporally Causal Discovery Tests for Discrete Time Series and Neural Spike Trains, IEEE Transactions on Signal Processing, 72, 1333-1347, doi.org/10.1109/TSP.2024.3371899
- Sapountzis P, Paneri S., Papadopoulos S., Gregoriou G.G. (2022) Dynamic and stable population coding of attentional instructions coexist in the prefrontal cortex, Proc Natl Acad Sci (PNAS), U.S.A., 119 (40), doi.org/10.1073/pnas.2202564119
- P. Sapountzis, S. Paneri and G.G. Gregoriou (2018) Distinct roles of prefrontal and parietal areas in the encoding of attentional priority. Proc Natl Acad Sci (PNAS), U.S.A., 115(37), doi.org/10.1073/pnas.1804643115
- P. Sapountzis, D. Schluppeck, R. Bowtell, J.W. Peirce. (2010) A comparison of fMRI adaptation and multi-variate pattern classification analysis in visual cortex. Neuroimage 49:1632–1640, doi.org/10.1016/j.neuroimage.2009.09.066
Research
His research investigates the neural circuit mechanisms underlying cognitive functions such as attention and working memory. To explore these mechanisms, he uses multi-area, laminar electrophysiological recordings, functional magnetic resonance imaging (fMRI) and psychophysical methods. He pairs these experimental approaches with computational tools, such as signal-processing techniques, artificial neural networks, and machine-learning approaches to characterize how cognitive functions are implemented at the level of neural populations and brain-wide networks.